# """ Vision Transformer (ViT) in PyTorch
# 
# A PyTorch implement of Vision Transformers as described in:
# 
# 'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
#     - https://arxiv.org/abs/2010.11929
# 
# `How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
#     - https://arxiv.org/abs/2106.10270
# 
# The official jax code is released and available at https://github.com/google-research/vision_transformer
# 
# Acknowledgments:
# * The paper authors for releasing code and weights, thanks!
# * I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
# for some einops/einsum fun
# * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
# * Bert reference code checks against Huggingface Transformers and Tensorflow Bert
# 
# Hacked together by / Copyright 2020, Ross Wightman
# """
# 
# import math
# import logging
# from functools import partial
# from collections import OrderedDict
# from typing import Optional
# 
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
# import torch.utils.checkpoint
# 
# from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
# from timm.models.helpers import build_model_with_cfg, resolve_pretrained_cfg, named_apply, adapt_input_conv, checkpoint_seq
# from timm.models.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_
# from timm.models.registry import register_model
# 
# _logger = logging.getLogger(__name__)
# 
# 
# def _cfg(url='', **kwargs):
#     return {
#         'url': url,
#         'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
#         'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
#         'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
#         'first_conv': 'patch_embed.proj', 'classifier': 'head',
#         **kwargs
#     }
# 
# 
# default_cfgs = {
#     # patch models (weights from official Google JAX impl)
#     'vit_tiny_patch16_224': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
#     'vit_tiny_patch16_384': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
#         input_size=(3, 384, 384), crop_pct=1.0),
#     'vit_small_patch32_224': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
#     'vit_small_patch32_384': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
#         input_size=(3, 384, 384), crop_pct=1.0),
#     'vit_small_patch16_224': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
#     'vit_small_patch16_384': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
#         input_size=(3, 384, 384), crop_pct=1.0),
#     'vit_base_patch32_224': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
#     'vit_base_patch32_384': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
#         input_size=(3, 384, 384), crop_pct=1.0),
#     'vit_base_patch16_224': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
#     'vit_base_patch16_384': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
#         input_size=(3, 384, 384), crop_pct=1.0),
#     'vit_base_patch8_224': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
#     'vit_large_patch32_224': _cfg(
#         url='',  # no official model weights for this combo, only for in21k
#         ),
#     'vit_large_patch32_384': _cfg(
#         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
#         input_size=(3, 384, 384), crop_pct=1.0),
#     'vit_large_patch16_224': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
#     'vit_large_patch16_384': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/'
#             'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
#         input_size=(3, 384, 384), crop_pct=1.0),
# 
#     'vit_large_patch14_224': _cfg(url=''),
#     'vit_huge_patch14_224': _cfg(url=''),
#     'vit_giant_patch14_224': _cfg(url=''),
#     'vit_gigantic_patch14_224': _cfg(url=''),
# 
# 
#     # patch models, imagenet21k (weights from official Google JAX impl)
#     'vit_tiny_patch16_224_in21k': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
#         num_classes=21843),
#     'vit_small_patch32_224_in21k': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
#         num_classes=21843),
#     'vit_small_patch16_224_in21k': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
#         num_classes=21843),
#     'vit_base_patch32_224_in21k': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0.npz',
#         num_classes=21843),
#     'vit_base_patch16_224_in21k': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
#         num_classes=21843),
#     'vit_base_patch8_224_in21k': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
#         num_classes=21843),
#     'vit_large_patch32_224_in21k': _cfg(
#         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',
#         num_classes=21843),
#     'vit_large_patch16_224_in21k': _cfg(
#         url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1.npz',
#         num_classes=21843),
#     'vit_huge_patch14_224_in21k': _cfg(
#         url='https://storage.googleapis.com/vit_models/imagenet21k/ViT-H_14.npz',
#         hf_hub_id='timm/vit_huge_patch14_224_in21k',
#         num_classes=21843),
# 
#     # SAM trained models (https://arxiv.org/abs/2106.01548)
#     'vit_base_patch32_224_sam': _cfg(
#         url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz'),
#     'vit_base_patch16_224_sam': _cfg(
#         url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz'),
# 
#     # DINO pretrained - https://arxiv.org/abs/2104.14294 (no classifier head, for fine-tune only)
#     'vit_small_patch16_224_dino': _cfg(
#         url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth',
#         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
#     'vit_small_patch8_224_dino': _cfg(
#         url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth',
#         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
#     'vit_base_patch16_224_dino': _cfg(
#         url='https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth',
#         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
#     'vit_base_patch8_224_dino': _cfg(
#         url='https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth',
#         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
# 
# 
#     # ViT ImageNet-21K-P pretraining by MILL
#     'vit_base_patch16_224_miil_in21k': _cfg(
#         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/vit_base_patch16_224_in21k_miil.pth',
#         mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
#     ),
#     'vit_base_patch16_224_miil': _cfg(
#         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm'
#             '/vit_base_patch16_224_1k_miil_84_4.pth',
#         mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear',
#     ),
# 
#     'vit_base_patch16_rpn_224': _cfg(
#         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_base_patch16_rpn_224-sw-3b07e89d.pth'),
# 
#     # experimental (may be removed)
#     'vit_base_patch32_plus_256': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95),
#     'vit_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240), crop_pct=0.95),
#     'vit_small_patch16_36x1_224': _cfg(url=''),
#     'vit_small_patch16_18x2_224': _cfg(url=''),
#     'vit_base_patch16_18x2_224': _cfg(url=''),
# }
# 
# 
# class Attention(nn.Module):
#     def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
#         super().__init__()
#         assert dim % num_heads == 0, 'dim should be divisible by num_heads'
#         self.num_heads = num_heads
#         head_dim = dim // num_heads
#         self.scale = head_dim ** -0.5
# 
#         self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
#         self.attn_drop = nn.Dropout(attn_drop)
#         self.proj = nn.Linear(dim, dim)
#         self.proj_drop = nn.Dropout(proj_drop)
# 
#     def forward(self, x, vis=True):
#         B, N, C = x.shape
#         qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
#         q, k, v = qkv.unbind(0)   # make torchscript happy (cannot use tensor as tuple)
#         feat = v
# 
#         attn = (q @ k.transpose(-2, -1)) * self.scale
#         attn = attn.softmax(dim=-1)
#         attn_ = attn # (400, 6, 197, 197)
#         attn = self.attn_drop(attn)
# 
#         x = (attn @ v).transpose(1, 2).reshape(B, N, C)
#         x = self.proj(x)
#         x = self.proj_drop(x)
# 
# 
#         ### Collect Data
#         qkv_col = qkv.unsqueeze(2).expand(3, B, self.num_heads, self.num_heads, N, C // self.num_heads)
#         qkv_row = qkv.unsqueeze(3).expand(3, B, self.num_heads, self.num_heads, N, C // self.num_heads)
#         qkv_dist = torch.norm((qkv_col - qkv_row), p=2, dim=[-1]).mean(dim=3)
# 
#         cos = nn.CosineSimilarity(dim=-1, eps=1e-6)
#         qkv_sim = cos(qkv_col, qkv_row).mean(dim=3)
# 
#         attn__ = attn_[:,:,0,1:] # (400, 6, 197, 197) → (400, 6, 196)
#         # B, H, N = attn_.shape
#         attn_col = attn__.unsqueeze(1).expand(B, self.num_heads, self.num_heads, N-1)
#         attn_row = attn__.unsqueeze(2).expand(B, self.num_heads, self.num_heads, N-1)
#         attn_dist = torch.norm((attn_col - attn_row), p=2, dim=[-1]).mean(dim=2)
# 
#         cos = nn.CosineSimilarity(dim=-1, eps=1e-6)
#         attn_sim = cos(attn_col, attn_row).mean(dim=2)
# 
#         attn_var = torch.var(attn__, dim=2)
# 
#         if vis:
#             return x, attn_.detach().cpu(), attn_var.detach().cpu(), attn_dist.detach().cpu(), attn_sim.detach().cpu(), qkv_dist.detach().cpu(), qkv_sim.detach().cpu(), feat
#         else:
#             return x
# 
# 
# class LayerScale(nn.Module):
#     def __init__(self, dim, init_values=1e-5, inplace=False):
#         super().__init__()
#         self.inplace = inplace
#         self.gamma = nn.Parameter(init_values * torch.ones(dim))
# 
#     def forward(self, x):
#         return x.mul_(self.gamma) if self.inplace else x * self.gamma
# 
# 
# class Block(nn.Module):
# 
#     def __init__(
#             self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., init_values=None,
#             drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
#         super().__init__()
#         self.norm1 = norm_layer(dim)
#         self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
#         self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
#         # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
#         self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
# 
#         self.norm2 = norm_layer(dim)
#         self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
#         self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
#         self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
# 
#     def forward(self, x):
# 
#         if type(x) == list:
#             x, attns, attns_var, attns_dist, attns_sim, qkvs_dist, qkvs_sim, features = x
#         else:
#             attns, attns_var, attns_dist, attns_sim, qkvs_dist, qkvs_sim, features = [], [], [], [], [], [], []
#         feature = x
#         tmp, attn, attn_var, attn_dist, attn_sim, qkv_dist, qkv_sim, _ = self.attn(self.norm1(x))
#         # tmp_ = tmp # for backward
# 
#         attns.append(attn)
#         attns_var.append(attn_var)
#         attns_dist.append(attn_dist)
#         attns_sim.append(attn_sim)
#         qkvs_dist.append(qkv_dist)
#         qkvs_sim.append(qkv_sim)
#         features.append(feature)
#         x = x + self.drop_path1(self.ls1(tmp))
#         x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
# 
#         return [x, attns, attns_var, attns_dist, attns_sim, qkvs_dist, qkvs_sim, features]
# 
# 
# class ResPostBlock(nn.Module):
# 
#     def __init__(
#             self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., init_values=None,
#             drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
#         super().__init__()
#         self.init_values = init_values
# 
#         self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
#         self.norm1 = norm_layer(dim)
#         self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
# 
#         self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
#         self.norm2 = norm_layer(dim)
#         self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
# 
#         self.init_weights()
# 
#     def init_weights(self):
#         # NOTE this init overrides that base model init with specific changes for the block type
#         if self.init_values is not None:
#             nn.init.constant_(self.norm1.weight, self.init_values)
#             nn.init.constant_(self.norm2.weight, self.init_values)
# 
#     def forward(self, x):
#         x = x + self.drop_path1(self.norm1(self.attn(x)))
#         x = x + self.drop_path2(self.norm2(self.mlp(x)))
#         return x
# 
# 
# class ParallelBlock(nn.Module):
# 
#     def __init__(
#             self, dim, num_heads, num_parallel=2, mlp_ratio=4., qkv_bias=False, init_values=None,
#             drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
#         super().__init__()
#         self.num_parallel = num_parallel
#         self.attns = nn.ModuleList()
#         self.ffns = nn.ModuleList()
#         for _ in range(num_parallel):
#             self.attns.append(nn.Sequential(OrderedDict([
#                 ('norm', norm_layer(dim)),
#                 ('attn', Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)),
#                 ('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
#                 ('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
#             ])))
#             self.ffns.append(nn.Sequential(OrderedDict([
#                 ('norm', norm_layer(dim)),
#                 ('mlp', Mlp(dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)),
#                 ('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
#                 ('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
#             ])))
# 
#     def _forward_jit(self, x):
#         x = x + torch.stack([attn(x) for attn in self.attns]).sum(dim=0)
#         x = x + torch.stack([ffn(x) for ffn in self.ffns]).sum(dim=0)
#         return x
# 
#     @torch.jit.ignore
#     def _forward(self, x):
#         x = x + sum(attn(x) for attn in self.attns)
#         x = x + sum(ffn(x) for ffn in self.ffns)
#         return x
# 
#     def forward(self, x):
#         if torch.jit.is_scripting() or torch.jit.is_tracing():
#             return self._forward_jit(x)
#         else:
#             return self._forward(x)
# 
# 
# class VisionTransformer(nn.Module):
#     """ Vision Transformer
# 
#     A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
#         - https://arxiv.org/abs/2010.11929
#     """
# 
#     def __init__(
#             self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='token',
#             embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=None,
#             class_token=True, no_embed_class=False, fc_norm=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
#             weight_init='', embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block):
#         """
#         Args:
#             img_size (int, tuple): input image size
#             patch_size (int, tuple): patch size
#             in_chans (int): number of input channels
#             num_classes (int): number of classes for classification head
#             global_pool (str): type of global pooling for final sequence (default: 'token')
#             embed_dim (int): embedding dimension
#             depth (int): depth of transformer
#             num_heads (int): number of attention heads
#             mlp_ratio (int): ratio of mlp hidden dim to embedding dim
#             qkv_bias (bool): enable bias for qkv if True
#             init_values: (float): layer-scale init values
#             class_token (bool): use class token
#             fc_norm (Optional[bool]): pre-fc norm after pool, set if global_pool == 'avg' if None (default: None)
#             drop_rate (float): dropout rate
#             attn_drop_rate (float): attention dropout rate
#             drop_path_rate (float): stochastic depth rate
#             weight_init (str): weight init scheme
#             embed_layer (nn.Module): patch embedding layer
#             norm_layer: (nn.Module): normalization layer
#             act_layer: (nn.Module): MLP activation layer
#         """
#         super().__init__()
#         assert global_pool in ('', 'avg', 'token')
#         assert class_token or global_pool != 'token'
#         use_fc_norm = global_pool == 'avg' if fc_norm is None else fc_norm
#         norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
#         act_layer = act_layer or nn.GELU
# 
#         self.num_classes = num_classes
#         self.global_pool = global_pool
#         self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
#         self.num_prefix_tokens = 1 if class_token else 0
#         self.no_embed_class = no_embed_class
#         self.grad_checkpointing = False
# 
#         self.patch_embed = embed_layer(
#             img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
#         num_patches = self.patch_embed.num_patches
# 
#         self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
#         embed_len = num_patches if no_embed_class else num_patches + self.num_prefix_tokens
#         self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * .02)
#         self.pos_drop = nn.Dropout(p=drop_rate)
# 
#         dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
#         self.blocks = nn.Sequential(*[
#             block_fn(
#                 dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, init_values=init_values,
#                 drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
#             for i in range(depth)])
#         self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
# 
#         # Classifier Head
#         self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
#         self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
# 
#         if weight_init != 'skip':
#             self.init_weights(weight_init)
# 
#         self.grad = False
# 
#     def init_weights(self, mode=''):
#         assert mode in ('jax', 'jax_nlhb', 'moco', '')
#         head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
#         trunc_normal_(self.pos_embed, std=.02)
#         if self.cls_token is not None:
#             nn.init.normal_(self.cls_token, std=1e-6)
#         named_apply(get_init_weights_vit(mode, head_bias), self)
# 
#     def _init_weights(self, m):
#         # this fn left here for compat with downstream users
#         init_weights_vit_timm(m)
# 
#     @torch.jit.ignore()
#     def load_pretrained(self, checkpoint_path, prefix=''):
#         _load_weights(self, checkpoint_path, prefix)
# 
#     @torch.jit.ignore
#     def no_weight_decay(self):
#         return {'pos_embed', 'cls_token', 'dist_token'}
# 
#     @torch.jit.ignore
#     def group_matcher(self, coarse=False):
#         return dict(
#             stem=r'^cls_token|pos_embed|patch_embed',  # stem and embed
#             blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
#         )
# 
#     @torch.jit.ignore
#     def set_grad_checkpointing(self, enable=True):
#         self.grad_checkpointing = enable
# 
#     @torch.jit.ignore
#     def get_classifier(self):
#         return self.head
# 
#     def reset_classifier(self, num_classes: int, global_pool=None):
#         self.num_classes = num_classes
#         if global_pool is not None:
#             assert global_pool in ('', 'avg', 'token')
#             self.global_pool = global_pool
#         self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
# 
#     def _pos_embed(self, x):
#         if self.no_embed_class:
#             # deit-3, updated JAX (big vision)
#             # position embedding does not overlap with class token, add then concat
#             x = x + self.pos_embed
#             if self.cls_token is not None:
#                 x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
#         else:
#             # original timm, JAX, and deit vit impl
#             # pos_embed has entry for class token, concat then add
#             if self.cls_token is not None:
#                 x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
#             x = x + self.pos_embed
#         return self.pos_drop(x)
# 
#     def forward_features(self, x):
#         x = self.patch_embed(x)
#         x = self._pos_embed(x)
#         if self.grad_checkpointing and not torch.jit.is_scripting():
#             x = checkpoint_seq(self.blocks, x)
#         else:
#             x = self.blocks(x)
#         x[0] = self.norm(x[0])
# 
#         return x
# 
#     def forward_head(self, x, pre_logits: bool = False):
#         if self.global_pool:
#             x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
#         x = self.fc_norm(x)
#         return x if pre_logits else self.head(x)
# 
#     def forward(self, x):
#         x = self.forward_features(x)
# 
#         x[0] = self.forward_head(x[0])
# 
#         return x if not self.grad else x[0]
# 
# 
# 
# def init_weights_vit_timm(module: nn.Module, name: str = ''):
#     """ ViT weight initialization, original timm impl (for reproducibility) """
#     if isinstance(module, nn.Linear):
#         trunc_normal_(module.weight, std=.02)
#         if module.bias is not None:
#             nn.init.zeros_(module.bias)
#     elif hasattr(module, 'init_weights'):
#         module.init_weights()
# 
# 
# def init_weights_vit_jax(module: nn.Module, name: str = '', head_bias: float = 0.):
#     """ ViT weight initialization, matching JAX (Flax) impl """
#     if isinstance(module, nn.Linear):
#         if name.startswith('head'):
#             nn.init.zeros_(module.weight)
#             nn.init.constant_(module.bias, head_bias)
#         else:
#             nn.init.xavier_uniform_(module.weight)
#             if module.bias is not None:
#                 nn.init.normal_(module.bias, std=1e-6) if 'mlp' in name else nn.init.zeros_(module.bias)
#     elif isinstance(module, nn.Conv2d):
#         lecun_normal_(module.weight)
#         if module.bias is not None:
#             nn.init.zeros_(module.bias)
#     elif hasattr(module, 'init_weights'):
#         module.init_weights()
# 
# 
# def init_weights_vit_moco(module: nn.Module, name: str = ''):
#     """ ViT weight initialization, matching moco-v3 impl minus fixed PatchEmbed """
#     if isinstance(module, nn.Linear):
#         if 'qkv' in name:
#             # treat the weights of Q, K, V separately
#             val = math.sqrt(6. / float(module.weight.shape[0] // 3 + module.weight.shape[1]))
#             nn.init.uniform_(module.weight, -val, val)
#         else:
#             nn.init.xavier_uniform_(module.weight)
#         if module.bias is not None:
#             nn.init.zeros_(module.bias)
#     elif hasattr(module, 'init_weights'):
#         module.init_weights()
# 
# 
# def get_init_weights_vit(mode='jax', head_bias: float = 0.):
#     if 'jax' in mode:
#         return partial(init_weights_vit_jax, head_bias=head_bias)
#     elif 'moco' in mode:
#         return init_weights_vit_moco
#     else:
#         return init_weights_vit_timm
# 
# 
# @torch.no_grad()
# def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
#     """ Load weights from .npz checkpoints for official Google Brain Flax implementation
#     """
#     import numpy as np
# 
#     def _n2p(w, t=True):
#         if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
#             w = w.flatten()
#         if t:
#             if w.ndim == 4:
#                 w = w.transpose([3, 2, 0, 1])
#             elif w.ndim == 3:
#                 w = w.transpose([2, 0, 1])
#             elif w.ndim == 2:
#                 w = w.transpose([1, 0])
#         return torch.from_numpy(w)
# 
#     w = np.load(checkpoint_path)
#     if not prefix and 'opt/target/embedding/kernel' in w:
#         prefix = 'opt/target/'
# 
#     if hasattr(model.patch_embed, 'backbone'):
#         # hybrid
#         backbone = model.patch_embed.backbone
#         stem_only = not hasattr(backbone, 'stem')
#         stem = backbone if stem_only else backbone.stem
#         stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
#         stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
#         stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
#         if not stem_only:
#             for i, stage in enumerate(backbone.stages):
#                 for j, block in enumerate(stage.blocks):
#                     bp = f'{prefix}block{i + 1}/unit{j + 1}/'
#                     for r in range(3):
#                         getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
#                         getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
#                         getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
#                     if block.downsample is not None:
#                         block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
#                         block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
#                         block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
#         embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
#     else:
#         embed_conv_w = adapt_input_conv(
#             model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
#     model.patch_embed.proj.weight.copy_(embed_conv_w)
#     model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
#     model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
#     pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
#     if pos_embed_w.shape != model.pos_embed.shape:
#         pos_embed_w = resize_pos_embed(  # resize pos embedding when different size from pretrained weights
#             pos_embed_w,
#             model.pos_embed,
#             getattr(model, 'num_prefix_tokens', 1),
#             model.patch_embed.grid_size
#         )
#     model.pos_embed.copy_(pos_embed_w)
#     model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
#     model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
#     if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
#         model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
#         model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
#     # NOTE representation layer has been removed, not used in latest 21k/1k pretrained weights
#     # if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
#     #     model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
#     #     model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
#     for i, block in enumerate(model.blocks.children()):
#         block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
#         mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
#         block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
#         block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
#         block.attn.qkv.weight.copy_(torch.cat([
#             _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
#         block.attn.qkv.bias.copy_(torch.cat([
#             _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
#         block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
#         block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
#         for r in range(2):
#             getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
#             getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
#         block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
#         block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))
# 
# 
# def resize_pos_embed(posemb, posemb_new, num_prefix_tokens=1, gs_new=()):
#     # Rescale the grid of position embeddings when loading from state_dict. Adapted from
#     # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
#     _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
#     ntok_new = posemb_new.shape[1]
#     if num_prefix_tokens:
#         posemb_prefix, posemb_grid = posemb[:, :num_prefix_tokens], posemb[0, num_prefix_tokens:]
#         ntok_new -= num_prefix_tokens
#     else:
#         posemb_prefix, posemb_grid = posemb[:, :0], posemb[0]
#     gs_old = int(math.sqrt(len(posemb_grid)))
#     if not len(gs_new):  # backwards compatibility
#         gs_new = [int(math.sqrt(ntok_new))] * 2
#     assert len(gs_new) >= 2
#     _logger.info('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new)
#     posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
#     posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bicubic', align_corners=False)
#     posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
#     posemb = torch.cat([posemb_prefix, posemb_grid], dim=1)
#     return posemb
# 
# 
# def checkpoint_filter_fn(state_dict, model, adapt_layer_scale=False):
#     """ convert patch embedding weight from manual patchify + linear proj to conv"""
#     import re
#     out_dict = {}
#     if 'model' in state_dict:
#         # For deit models
#         state_dict = state_dict['model']
# 
#     for k, v in state_dict.items():
#         if 'patch_embed.proj.weight' in k and len(v.shape) < 4:
#             # For old models that I trained prior to conv based patchification
#             O, I, H, W = model.patch_embed.proj.weight.shape
#             v = v.reshape(O, -1, H, W)
#         elif k == 'pos_embed' and v.shape[1] != model.pos_embed.shape[1]:
#             # To resize pos embedding when using model at different size from pretrained weights
#             v = resize_pos_embed(
#                 v,
#                 model.pos_embed,
#                 0 if getattr(model, 'no_embed_class') else getattr(model, 'num_prefix_tokens', 1),
#                 model.patch_embed.grid_size
#             )
#         elif adapt_layer_scale and 'gamma_' in k:
#             # remap layer-scale gamma into sub-module (deit3 models)
#             k = re.sub(r'gamma_([0-9])', r'ls\1.gamma', k)
#         elif 'pre_logits' in k:
#             # NOTE representation layer removed as not used in latest 21k/1k pretrained weights
#             continue
#         out_dict[k] = v
#     return out_dict
# 
# 
# def _create_vision_transformer(variant, pretrained=False, **kwargs):
#     if kwargs.get('features_only', None):
#         raise RuntimeError('features_only not implemented for Vision Transformer models.')
# 
#     pretrained_cfg = resolve_pretrained_cfg(variant, pretrained_cfg=kwargs.pop('pretrained_cfg', None))
#     model = build_model_with_cfg(
#         VisionTransformer, variant, pretrained,
#         pretrained_cfg=pretrained_cfg,
#         pretrained_filter_fn=checkpoint_filter_fn,
#         pretrained_custom_load='npz' in pretrained_cfg['url'],
#         **kwargs)
#     return model
# 
# 
# @register_model
# def vit_tiny_patch16_224(pretrained=False, **kwargs):
#     """ ViT-Tiny (Vit-Ti/16)
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
#     model = _create_vision_transformer('vit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_tiny_patch16_384(pretrained=False, **kwargs):
#     """ ViT-Tiny (Vit-Ti/16) @ 384x384.
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
#     model = _create_vision_transformer('vit_tiny_patch16_384', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_small_patch32_224(pretrained=False, **kwargs):
#     """ ViT-Small (ViT-S/32)
#     """
#     model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
#     model = _create_vision_transformer('vit_small_patch32_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_small_patch32_384(pretrained=False, **kwargs):
#     """ ViT-Small (ViT-S/32) at 384x384.
#     """
#     model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
#     model = _create_vision_transformer('vit_small_patch32_384', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_small_patch16_224(pretrained=False, **kwargs):
#     """ ViT-Small (ViT-S/16)
#     NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
#     model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_small_patch16_384(pretrained=False, **kwargs):
#     """ ViT-Small (ViT-S/16)
#     NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
#     model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch32_224(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-1k weights fine-tuned from in21k, source https://github.com/google-research/vision_transformer.
#     """
#     model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch32_384(pretrained=False, **kwargs):
#     """ ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
#     """
#     model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch32_384', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch16_224(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch16_384(pretrained=False, **kwargs):
#     """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch8_224(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/8) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
#     """
#     model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch8_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_large_patch32_224(pretrained=False, **kwargs):
#     """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
#     """
#     model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
#     model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_large_patch32_384(pretrained=False, **kwargs):
#     """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
#     """
#     model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
#     model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_large_patch16_224(pretrained=False, **kwargs):
#     """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
#     model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_large_patch16_384(pretrained=False, **kwargs):
#     """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
#     model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_large_patch14_224(pretrained=False, **kwargs):
#     """ ViT-Large model (ViT-L/14)
#     """
#     model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, **kwargs)
#     model = _create_vision_transformer('vit_large_patch14_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_huge_patch14_224(pretrained=False, **kwargs):
#     """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
#     """
#     model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, **kwargs)
#     model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_giant_patch14_224(pretrained=False, **kwargs):
#     """ ViT-Giant model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
#     """
#     model_kwargs = dict(patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, **kwargs)
#     model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_gigantic_patch14_224(pretrained=False, **kwargs):
#     """ ViT-Gigantic model (ViT-G/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
#     """
#     model_kwargs = dict(patch_size=14, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16, **kwargs)
#     model = _create_vision_transformer('vit_gigantic_patch14_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
#     """ ViT-Tiny (Vit-Ti/16).
#     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
#     NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
#     model = _create_vision_transformer('vit_tiny_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_small_patch32_224_in21k(pretrained=False, **kwargs):
#     """ ViT-Small (ViT-S/16)
#     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
#     NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
#     """
#     model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
#     model = _create_vision_transformer('vit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_small_patch16_224_in21k(pretrained=False, **kwargs):
#     """ ViT-Small (ViT-S/16)
#     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
#     NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
#     model = _create_vision_transformer('vit_small_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
#     """ ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
#     NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
#     """
#     model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
#     """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
#     NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch8_224_in21k(pretrained=False, **kwargs):
#     """ ViT-Base model (ViT-B/8) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
#     NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
#     """
#     model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch8_224_in21k', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
#     """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
#     NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
#     """
#     model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
#     model = _create_vision_transformer('vit_large_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
#     """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
#     NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
#     model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
#     """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
#     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
#     NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
#     """
#     model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, **kwargs)
#     model = _create_vision_transformer('vit_huge_patch14_224_in21k', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch16_224_sam(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/16) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch16_224_sam', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch32_224_sam(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/32) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
#     """
#     model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch32_224_sam', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_small_patch16_224_dino(pretrained=False, **kwargs):
#     """ ViT-Small (ViT-S/16) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
#     model = _create_vision_transformer('vit_small_patch16_224_dino', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_small_patch8_224_dino(pretrained=False, **kwargs):
#     """ ViT-Small (ViT-S/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
#     """
#     model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6, **kwargs)
#     model = _create_vision_transformer('vit_small_patch8_224_dino', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch16_224_dino(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/16) /w DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch16_224_dino', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch8_224_dino(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
#     """
#     model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
#     model = _create_vision_transformer('vit_base_patch8_224_dino', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch16_224_miil_in21k(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
#     Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
#     model = _create_vision_transformer('vit_base_patch16_224_miil_in21k', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch16_224_miil(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
#     Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
#     model = _create_vision_transformer('vit_base_patch16_224_miil', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# # Experimental models below
# 
# @register_model
# def vit_base_patch32_plus_256(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/32+)
#     """
#     model_kwargs = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
#     model = _create_vision_transformer('vit_base_patch32_plus_256', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch16_plus_240(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/16+)
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
#     model = _create_vision_transformer('vit_base_patch16_plus_240', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch16_rpn_224(pretrained=False, **kwargs):
#     """ ViT-Base (ViT-B/16) w/ residual post-norm
#     """
#     model_kwargs = dict(
#         patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5, class_token=False,
#         block_fn=ResPostBlock, global_pool=kwargs.pop('global_pool', 'avg'), **kwargs)
#     model = _create_vision_transformer('vit_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_small_patch16_36x1_224(pretrained=False, **kwargs):
#     """ ViT-Base w/ LayerScale + 36 x 1 (36 block serial) config. Experimental, may remove.
#     Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
#     Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
#     """
#     model_kwargs = dict(patch_size=16, embed_dim=384, depth=36, num_heads=6, init_values=1e-5, **kwargs)
#     model = _create_vision_transformer('vit_small_patch16_36x1_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_small_patch16_18x2_224(pretrained=False, **kwargs):
#     """ ViT-Small w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
#     Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
#     Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
#     """
#     model_kwargs = dict(
#         patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelBlock, **kwargs)
#     model = _create_vision_transformer('vit_small_patch16_18x2_224', pretrained=pretrained, **model_kwargs)
#     return model
# 
# 
# @register_model
# def vit_base_patch16_18x2_224(pretrained=False, **kwargs):
#     """ ViT-Base w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
#     Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
#     """
#     model_kwargs = dict(
#         patch_size=16, embed_dim=768, depth=18, num_heads=12, init_values=1e-5, block_fn=ParallelBlock, **kwargs)
#     model = _create_vision_transformer('vit_base_patch16_18x2_224', pretrained=pretrained, **model_kwargs)
#     return model
